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Chris Wiggins
Columbia University
Predictive Modeling of Network Evolution and Genetic Expression
May 4, 2006 10:00am
Abstract:
High-throughput biological experiments now yield copious gene expression data, sequence data, and network data, posing novel challenges to the theoretical and computational community hoping to learn biology from these abundant but heterogeneous datasets. In this talk I hope to illustrate how machine learning approaches can be brought to bear on two such network-level problems in systems biology: `reverse-engineering` biological networks from microarray and sequence data (including revealing sequence `motifs'), and revealing which of several competing evolutionary design principles best describes observed network topologies. By posing these problems as classification tasks, predictive and interpretable models are obtained. Depending on whether the audience turns out to be computer scientists or biologists, I will focus either on how to turn biology into boosting, or vice versa.
If you have questions, or would like to meet the speaker, please contact Ponda at 4-1994 or pondabarnes@tti-c.org. For information on future TTI-C talks or events, please go to the TTI-C Events page.